Mitig Adapt Strat Glob Change (2007) 12:1001–1026 DOI 10.1007/s11027-006-9062-5 ORIGINAL PAPER
Baselines for land-use change in the tropics: application to avoided deforestation projects Sandra Brown Æ Myrna Hall Æ Ken Andrasko Æ Fernando Ruiz Æ Walter Marzoli Æ Gabriela Guerrero Æ Omar Masera Æ Aaron Dushku Æ Ben DeJong Æ Joseph Cornell
Received: 20 June 2006 / Accepted: 11 August 2006 / Published online: 30 March 2007 Springer Science+Business Media B.V. 2007
Abstract Although forest conservation activities, particularly in the tropics, offer significant potential for mitigating carbon (C) emissions, these types of activities have faced obstacles in the policy arena caused by the difficulty in determining key elements of the project cycle, particularly the baseline. A baseline for forest conservation has two main components: the projected land-use change and the corresponding carbon stocks in applicable pools in vegetation and soil, with land-use change being the most difficult to address analytically. In this paper we focus on developing and comparing three models, ranging from relatively simple extrapolations of past trends in land use based on simple
S. Brown (&) A. Dushku Winrock International, 1621 N. Kent St., Suite 1200, Arlington, VA 22209, USA e-mail:
[email protected] M. Hall J. Cornell SUNY College of Environmental Science and Forestry, 1 Forestry Drive, Syracuse, NY 13210, USA K. Andrasko OAP/Climate Change Div., US Environmental Protection Agency, 6202J, 1200 Pennsylvania Ave., NW, Washington, DC 20460, USA F. Ruiz Consejo Civil Mexicano para la Silvicultura Sostenible, A.C., Miguel Angel de Quevedo 103 Col. Chimalistac, Mexico D.F. 01070, Mexico W. Marzoli Piazza S. Croce in Gerusalemme, 1, 00185 Roma, Italy G. Guerrero O. Masera Instituto de Ecologia, Universidad Nacional Autonoma de Mexico, Morelia, Michoaca´n, Mexico B. DeJong El Colegio de la Frontera Sur, Unidad Villahermosa, Apartado postal 1042, Col Atasta, Villahermosa, Tabasco, Mexico
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drivers such as population growth to more complex extrapolations of past trends using spatially explicit models of land-use change driven by biophysical and socioeconomic factors. The three models used for making baseline projections of tropical deforestation at the regional scale are: the Forest Area Change (FAC) model, the Land Use and Carbon Sequestration (LUCS) model, and the Geographical Modeling (GEOMOD) model. The models were used to project deforestation in six tropical regions that featured different ecological and socioeconomic conditions, population dynamics, and uses of the land: (1) northern Belize; (2) Santa Cruz State, Bolivia; (3) Parana´ State, Brazil; (4) Campeche, Mexico; (5) Chiapas, Mexico; and (6) Michoaca´n, Mexico.A comparison of all model outputs across all six regions shows that each model produced quite different deforestation baselines. In general, the simplest FAC model, applied at the national administrative-unit scale, projected the highest amount of forest loss (four out of six regions) and the LUCS model the least amount of loss (four out of five regions). Based on simulations of GEOMOD, we found that readily observable physical and biological factors as well as distance to areas of past disturbance were each about twice as important as either sociological/ demographic or economic/infrastructure factors (less observable) in explaining empirical land-use patterns.We propose from the lessons learned, a methodology comprised of three main steps and six tasks can be used to begin developing credible baselines. We also propose that the baselines be projected over a 10-year period because, although projections beyond 10 years are feasible, they are likely to be unrealistic for policy purposes. In the first step, an historic land-use change and deforestation estimate is made by determining the analytic domain (size of the region relative to the size of proposed project), obtaining historic data, analyzing candidate baseline drivers, and identifying three to four major drivers. In the second step, a baseline of where deforestation is likely to occur–a potential land-use change (PLUC) map—is produced using a spatial model such as GEOMOD that uses the key drivers from step one. Then rates of deforestation are projected over a 10-year baseline period based on one of the three models. Using the PLUC maps, projected rates of deforestation, and carbon stock estimates, baseline projections are developed that can be used for project GHG accounting and crediting purposes: The final step proposes that, at agreed interval (e.g., about 10 years), the baseline assumptions about baseline drivers be re-assessed. This step reviews the viability of the 10-year baseline in light of changes in one or more key baseline drivers (e.g., new roads, new communities, new protected area, etc.). The potential land-use change map and estimates of rates of deforestation could be re-done at the agreed interval, allowing the deforestation rates and changes in spatial drivers to be incorporated into a defense of the existing baseline, or the derivation of a new baseline projection. Keywords Avoided deforestation Carbon sequestration Land-use change Forestry GEOMOD LULUCF Tropics
1 Introduction On a global scale, land-use change and forestry activities have historically been, and are currently, net sources of carbon dioxide to the atmosphere. During the decade of the 1990s, carbon dioxide (CO2) emissions to the atmosphere caused by changes in land use were estimated to be 1.6 billion t C/year (Bolin and Sukumar 2000), with tropical deforestation
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essentially responsible for most of this source. Activities that reduce deforestation rates, increase forestation, or improve land use efficiency offer significant potential for mitigating greenhouse gas (GHG) emissions, thereby reducing the potential impacts of climate change. Through projects and policies that change forest and other land management practices, humans have the potential to change the direction and magnitude of the flux of carbon dioxide between the land and atmosphere. At the same time these changes can provide multiple co-benefits to meet environmental and socioeconomic goals of sustainable development. Afforestation and reforestation projects are generally accepted as projects that can generate tradable greenhouse gas (GHG) emission reductions (e.g., under the UN Framework Convention on Climate Change (UN FCCC) Kyoto Protocol). Forest conservation projects, on the other hand, have faced obstacles to acceptance due to the difficulty in determining key elements of the project cycle. For instance, some have argued that determining baselines for forest conservation projects is too difficult and uncertain. Others have raised objections with respect to ‘‘leakage’’ (i.e., the off-site effects of project activities on carbon stocks and GHG emissions) (Brown et al. 2000b). Without inclusion of projects that are designed to avoid deforestation and improve the sustainability of agriculture in developing countries, a large opportunity is lost (Niles et al. 2002). At the same time, many countries continue to be interested in developing forest conservation projects given the potential for such projects to slow or even reverse high rates of deforestation that could generate credible GHG emission reductions. Given the challenge of addressing important analytical issues related to and the continuing interest in forest conservation projects, we look at issues related to these project types. A fundamental and challenging component of all project activities, and avoided deforestation projects specifically, is the determination of the extent to which project interventions lead to GHG benefits that are additional to business-as-usual scenarios (i.e., the baseline scenarios). The development of a baseline is a key step in the implementation of land use, land-use change, and forestry (LULUCF) projects to ensure accurate crediting of their carbon impacts (OECD/IEA 2003) because GHG benefits of a project activity are computed as the difference in carbon stocks and other GHG emissions of the project activity and the baseline. A key issue therefore, is how to develop a baseline scenario for avoided deforestation that reasonably represents the net emissions without the project. There are currently no standard practices for developing baselines for avoided deforestation projects. A baseline has two major components: the projected land-use or landcover change, and the corresponding carbon stocks in live and dead vegetation and soil. Of the two components needed for baselines, the projections of changes in land use are the most important and yet the most difficult to address analytically (OECD/IEA 2003) because many socioeconomic and environmental factors affect the way people use land and these are difficult to predict. And, once a project to reduce deforestation is implemented the rate and pattern of land-use change in the project area can no longer be monitored. Existing baseline estimates are limited by the absence of agreed standardized methods. For many of the existing pilot forestry-based carbon projects, estimates of changes in land use and baselines were determined on a project-by-project approach using simple logical arguments that assumed continuation of observed past trends for the limited project area or a region. These projects generally did not use analytically rigorous and transparent agreed
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methods because they did not exist at the time and were not required by voluntary programs to which the projects were reported as demonstrations (Brown et al. 2000b; OECD/ IEA 2003). They also did not test alternative baseline approaches. In addition, this projectby-project approach is likely to increase investment costs, further undermining the potential for developing these kinds of projects (OECD/IEA 2003). The result is the perception of LULUCF baselines as subjective projections of land-use change and hence GHG mitigation potential with high uncertainty, high cost per unit of carbon benefit, and a lack of transparency. Developing regional baselines for the land-use component by project-activity type offers an alternative to the project-by-project approach (also called the performance standard approach in the World Resources Institute/World Business Council for Sustainable Development 2003 project protocols). Regional baselines are projections of the magnitude and in some cases spatial depiction of one or more land-use change activities (e.g., forestation, deforestation) over a region in which a potential mitigation project could be located. These baselines would use regional data and transparent analytic assumptions not derived from a specific project, to set a generic baseline for the defined class of activity. This baseline can be either spatially resolved (e.g., a projection for specific pixels or lands), or an average rate of change over time for the activity in that region. The concept was pioneered by the Scolel Te project team in Chiapas State, southern Mexico, which developed several alternative, spatially resolved, baselines projected out 50 years for about half of the state, (Tipper and De Jong 1998; Tipper et al. 1998). Regional baselines may have several advantages, including: reduced investment cost to develop compared to project-specific baselines; consideration of regional factors that could affect land-use changes; and opportunity for host country or state governments to identify the effects of and target the type of projects supportive of their sustainable development. Use of regional baselines is likely to result in more transparent and credible baselines. Although regional baselines have the advantages presented here, there is potentially a major disadvantage if they are not spatially resolved. Project developers could identify areas where deforestation appears likely to be lower than the regional baseline thus getting more carbon credits than were actually being generated. At the other extreme, areas that appear to have potentially higher deforestation rates than the baseline would be avoided because the carbon credits would be underestimated. For the carbon stocks, most pilot projects based their baselines on estimates from the scientific literature in combination with some field measurements in nearby areas. The use of estimates from the literature for the carbon stocks is a reasonable first approximation for the baseline. Unlike the land-use change component of the baseline, the carbon stocks can be monitored over the length of the project. Thus, once a project area is selected, the carbon stocks can be monitored at that locale, the first approximation revised, and a more project-specific baseline can be produced. Carbon stocks and their changes in above- and below-ground biomass, on a unit area basis, can be measured under many circumstances to relatively high levels of accuracy and precision at a modest cost (95% confidence intervals of less than ±10% of the mean, at an estimated cost of about
